The Hopfield model [1]} is a model of associative memory and it has been used as a prototype of artificial neural networks that, because of the increased computational power, have recently been widely exploited as the most powerful and flexible tools of machine learning  [2]}, [3]}. It is, then, of great importance to understand the mechanisms underlying models of neural networks. One of such mechanisms, that requires a deeper understanding, is the ability of a neural network model to learn mismatched patterns, that is patterns made of variables of a different nature than those of the model. For examples, the classical Hopfield model is defined in terms of discrete variables (Ising spins), but the performance of the model when the memory to recover has a different kind of variables is not yet clear. This is the well known mismatching problem  [4]}, [5]}, [6]} that is relevant in realistic applications  [7]}.
